Light Field Spatial Resolution Enhancement Framework

• URL: http://arxiv.org/abs/2405.02787v1
• Date: Sun, 5 May 2024 02:07:10 GMT
• Title: Light Field Spatial Resolution Enhancement Framework
• Authors: Javeria Shabbir, Muhammad Zeshan. Alam, M. Umair Mukati,
• Abstract summary: We propose a novel light field framework for resolution enhancement. The first module generates a high-resolution, all-in-focus image. The second module, a texture transformer network, enhances the resolution of each light field perspective independently.
• Score: 0.24578723416255746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Light field (LF) imaging captures both angular and spatial light distributions, enabling advanced photographic techniques. However, micro-lens array (MLA)- based cameras face a spatial-angular resolution tradeoff due to a single shared sensor. We propose a novel light field framework for resolution enhancement, employing a modular approach. The first module generates a high-resolution, all-in-focus image. The second module, a texture transformer network, enhances the resolution of each light field perspective independently using the output of the first module as a reference image. The final module leverages light field regularity to jointly improve resolution across all LF image perspectives. Our approach demonstrates superior performance to existing methods in both qualitative and quantitative evaluations.

Related papers

• LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction [5.461017270708014]
  We propose a lightweight model named LGFN which integrates the local and global features of different views and the features of different channels for LF image SR. Our model has a parameter of 0.45M and a FLOPs of 19.33G which has achieved a competitive effect.
  arXiv  Detail & Related papers  (2024-09-26T11:53:25Z)
• Unsupervised Learning of High-resolution Light Field Imaging via Beam Splitter-based Hybrid Lenses [42.5604477188514]
  We design a beam splitter-based hybrid light field imaging prototype to record 4D light field image and high-resolution 2D image simultaneously. The 2D image could be considered as the high-resolution ground truth corresponding to the low-resolution central sub-aperture image of 4D light field image. We propose an unsupervised learning-based super-resolution framework with the hybrid light field dataset.
  arXiv  Detail & Related papers  (2024-02-29T10:30:02Z)
• Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network [52.77569396659629]
  This paper presents the deep compensation network unfolding (DCUNet) for restoring light field (LF) images captured under low-light conditions. The framework uses the intermediate enhanced result to estimate the illumination map, which is then employed in the unfolding process to produce a new enhanced result. To properly leverage the unique characteristics of LF images, this paper proposes a pseudo-explicit feature interaction module.
  arXiv  Detail & Related papers  (2023-08-10T07:53:06Z)
• Learning Texture Transformer Network for Light Field Super-Resolution [1.5469452301122173]
  We propose a method to improve the spatial resolution of light field images with the aid of the Transformer Network (TTSR) The results demonstrate around 4 dB to 6 dB PSNR gain over a bicubically resized light field image.
  arXiv  Detail & Related papers  (2022-10-09T15:16:07Z)
• Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging [17.721259583120396]
  This paper proposes an adaptation module in a pretrained Single Image Super Resolution (SISR) network to leverage the powerful SISR model. It is an adaptation in the SISR network to further exploit the spatial and angular information in LF images to improve the super resolution performance.
  arXiv  Detail & Related papers  (2022-07-25T03:43:56Z)
• Dual-Camera Super-Resolution with Aligned Attention Modules [56.54073689003269]
  We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR) Our proposed method generalizes the standard patch-based feature matching with spatial alignment operations. To bridge the domain gaps between real-world images and the training images, we propose a self-supervised domain adaptation strategy.
  arXiv  Detail & Related papers  (2021-09-03T07:17:31Z)
• Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses [67.01164492518481]
  This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses. We propose a novel end-to-end learning-based approach, which can comprehensively utilize the specific characteristics of the input. Our framework could potentially decrease the cost of high-resolution LF data acquisition and benefit LF data storage and transmission.
  arXiv  Detail & Related papers  (2021-02-14T06:44:47Z)
• Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution [93.95828097088608]
  Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution. The high-dimensionality characteristic and complex geometrical structure of LF images make the problem more challenging than traditional single-image SR. We propose a novel learning-based LF spatial SR framework to explore the coherence among LF sub-aperture images. Experimental results over both synthetic and real-world LF datasets demonstrate the significant advantage of our approach over state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-26T08:34:37Z)
• Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization [99.96632216070718]
  Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution. The high-dimensional spatiality characteristic and complex geometrical structure of LF images make the problem more challenging than traditional single-image SR. We propose a novel learning-based LF framework, in which each view of an LF image is first individually super-resolved.
  arXiv  Detail & Related papers  (2020-04-05T14:39:57Z)
• Learning Light Field Angular Super-Resolution via a Geometry-Aware Network [101.59693839475783]
  We propose an end-to-end learning-based approach aiming at angularly super-resolving a sparsely-sampled light field with a large baseline. Our method improves the PSNR of the second best method up to 2 dB in average, while saves the execution time 48$times$.
  arXiv  Detail & Related papers  (2020-02-26T02:36:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.